Balancing Transferability and Imperceptibility for Adversarial Attacks

Data-driven deep learning models face the problem of well-designed adversarial attacks due to their inability to cover all possible sample data.The existing main <i>L_p</i>-norm perturbation attack methods based on RGB pixel space have achieved great attack success rates and transferability,but the generated adversarial samples have high-frequency noise that is easily perceived by the human eye.The attack methods based on diffusion models balance transferability and imperceptibility,but their optimization strategies mainly focus on the perspective of adversarial models.Those researches lack deep exploration and analysis of transfer-ability and imperceptibility from the perspective of surrogate model.In order to further explore and analyze the control sources of transferability and imperceptibility,a new adversarial sample generation method based on latent diffusion model is proposed within the framework of an attack method based on surrogate model.In this method,under the constraint of basic adversarial loss,transferable attention constraint loss and imperceptible consistency constraint loss are designed to achieve a balance between transferability and imperceptibility.On three publicly available datasets,ImageNet Compatible,CUB-200-2011,and Stanford Cars,compared with existing methods,the proposed method generates adversarial samples with strong cross-model transferable attack ability and the effect of imperceptible disturbance to the human eye.